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NASA NTRS2008

Bayesian Inference in the Modern Design of Experiments

This paper provides an elementary tutorial overview of Bayesian inference and its potential for application in aerospace experimentation in general and wind tunnel testing in particular. Bayes Theorem is reviewed and examples are provided to illustrate how it can be applied to objectively revise prior knowledge by incorporating insights subsequently obtained from additional observations, resulting in new (posterior) knowledge that combines information from both sources. A logical merger of Bayesian methods and certain aspects of Response Surface Modeling is explored. Specific applications to wind tunnel testing, computational code validation, and instrumentation calibration are discussed.

DeLoach, Richard↗
NASA NTRS2007

An Alternative Interpretation of the Relationship between the Inferred Open Solar Flux and the Interplanetary Magnetic Field

Photospheric observations at the Wilcox Solar Observatory (WSO) represent an uninterrupted data set of 32 years and are therefore unique for modeling variations in the magnetic structure of the corona and inner heliosphere over three solar cycles. For many years, modelers have applied a latitudinal correction factor to these data, believing that it provided a better estimate of the line-of-sight magnetic field. Its application was defended by arguing that the computed open flux matched observations of the interplanetary magnetic field (IMF) significantly better than the original WSO correction factor. However, no physically based argument could be made for its use. In this Letter we explore the implications of using the constant correction factor on the value and variation of the computed open solar flux and its relationship to the measured IMF. We find that it does not match the measured IMF at 1 AU except at and surrounding solar minimum. However, we argue that interplanetary coronal mass ejections (ICMEs) may provide sufficient additional magnetic flux to the extent that a remarkably good match is found between the sum of the computed open flux and inferred ICME flux and the measured flux at 1 AU. If further substantiated, the implications of this interpretation may be significant, including a better understanding of the structure and strength of the coronal field and I N providing constraints for theories of field line transport in the corona, the modulation of galactic cosmic rays, and even possibly terrestrial climate effects.

Riley, Pete↗
NASA NTRS2006

Land Water Storage within the Congo Basin Inferred from GRACE Satellite Gravity Data

GRACE satellite gravity data is used to estimate terrestrial (surface plus ground) water storage within the Congo Basin in Africa for the period of April, 2002 - May, 2006. These estimates exhibit significant seasonal (30 +/- 6 mm of equivalent water thickness) and long-term trends, the latter yielding a total loss of approximately 280 km(exp 3) of water over the 50-month span of data. We also combine GRACE and precipitation data set (CMAP, TRMM) to explore the relative contributions of the source term to the seasonal hydrological balance within the Congo Basin. We find that the seasonal water storage tends to saturate for anomalies greater than 30-44 mm of equivalent water thickness. Furthermore, precipitation contributed roughly three times the peak water storage after anomalously rainy seasons, in early 2003 and 2005, implying an approximately 60-70% loss from runoff and evapotranspiration. Finally, a comparison of residual land water storage (monthly estimates minus best-fitting trends) in the Congo and Amazon Basins shows an anticorrelation, in agreement with the 'see-saw' variability inferred by others from runoff data.

Crowley, John W.↗
NASA NTRS2008

Inferring the Energy Distribution of Accelerated Electrons in Solar Flares from X-ray Observations

Knowledge of the energy distribution of electrons accelerated in solar flares is important for constraining possible acceleration mechanisms and for understanding the relationships between flare X-ray sources, radio sources, and particles observed in space. Solar flare hard X-rays are primarily emitted from dense, thick-target regions in the lower atmosphere, but the electrons are understood to be accelerated higher in the corona. Various processes can distort the X-ray spectrum or the energy distribution of electrons before they reach the thick-target region. After briefly reviewing the processes that affect the X-ray spectrum and the electron distribution, I will describe recent results from a study of flare spectra from RHESSI to determine the importance of these processes in inferring the energy distribution of accelerated electrons.

Holman, Gordon D.↗
NASA NTRS2009

Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

Dezfuli, Homayoon↗
NASA NTRS2009

Understanding the Scalability of Bayesian Network Inference using Clique Tree Growth Curves

Bayesian networks (BNs) are used to represent and efficiently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to perform computation in BNs is clique tree clustering and propagation. In this approach, BN computation consists of propagation in a clique tree compiled from a Bayesian network. There is a lack of understanding of how clique tree computation time, and BN computation time in more general, depends on variations in BN size and structure. On the one hand, complexity results tell us that many interesting BN queries are NP-hard or worse to answer, and it is not hard to find application BNs where the clique tree approach in practice cannot be used. On the other hand, it is well-known that tree-structured BNs can be used to answer probabilistic queries in polynomial time. In this article, we develop an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN's non-root nodes to the number of root nodes, or (ii) the expected number of moral edges in their moral graphs. Our approach is based on combining analytical and experimental results. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for each set. For the special case of bipartite BNs, we consequently have two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, we systematically increase the degree of the root nodes in bipartite Bayesian networks, and find that root clique growth is well-approximated by Gompertz growth curves. It is believed that this research improves the understanding of the scaling behavior of clique tree clustering, provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms, and presents an aid for analytical trade-off studies of clique tree clustering using growth curves.

Mengshoel, Ole Jakob↗
NASA NTRS2010

Inferring Cirrus Size Distributions Through Satellite Remote Sensing and Microphysical Databases

Since cirrus clouds have a substantial influence on the global energy balance that depends on their microphysical properties, climate models should strive to realistically characterize the cirrus ice particle size distribution (PSD), at least in a climatological sense. To date, the airborne in situ measurements of the cirrus PSD have contained large uncertainties due to errors in measuring small ice crystals (D<60 m). This paper presents a method to remotely estimate the concentration of the small ice crystals relative to the larger ones using the 11- and 12- m channels aboard several satellites. By understanding the underlying physics producing the emissivity difference between these channels, this emissivity difference can be used to infer the relative concentration of small ice crystals. This is facilitated by enlisting temperature-dependent characterizations of the PSD (i.e., PSD schemes) based on in situ measurements. An average cirrus emissivity relationship between 12 and 11 m is developed here using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and is used to retrieve the PSD based on six different PSD schemes. The PSDs from the measurement-based PSD schemes are compared with corresponding retrieved PSDs to evaluate differences in small ice crystal concentrations. The retrieved PSDs generally had lower concentrations of small ice particles, with total number concentration independent of temperature. In addition, the temperature dependence of the PSD effective diameter De and fall speed Vf for these retrieved PSD schemes exhibited less variability relative to the unmodified PSD schemes. The reduced variability in the retrieved De and Vf was attributed to the lower concentrations of small ice crystals in the retrieved PSD.

Mitchell, David↗
NASA NTRS2010

Understanding the Scalability of Bayesian Network Inference Using Clique Tree Growth Curves

One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a Bayesian network, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN s non-root nodes to the number of root nodes, and (ii) the expected number of moral edges in their moral graphs. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for the total size of each set. For the special case of bipartite BNs, there are two sets and two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes. Surprisingly, root clique growth is well-approximated by Gompertz growth curves, an S-shaped family of curves that has previously been used to describe growth processes in biology, medicine, and neuroscience. We believe that this research improves the understanding of the scaling behavior of clique tree clustering for a certain class of Bayesian networks; presents an aid for trade-off studies of clique tree clustering using growth curves; and ultimately provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms.

Mengshoel, Ole J.↗
NASA NTRS2012

Can We Infer Ocean Dynamics from Altimeter Wavenumber Spectra?

The wavenumber spectra of sea surface height (SSH) and kinetic energy (KE) have been used to infer the dynamics of the ocean. When quasi-geostrophic dynamics (QG) or surface quasi-geostrophic (SQG) turbulence dominate and an inertial subrange exists, a steep SSH wavenumber spectrum is expected with k-5 for QG turbulence and a flatter k-11/3 for SQG turbulence. However, inspection of the spectral slopes in the mesoscale band of 70 to 250 km shows that the altimeter wavenumber slopes typically are much flatter than the QG or SQG predictions over most of the ocean. Comparison of the altimeter wavenumber spectra with the spectra estimated from the output of an eddy resolving global ocean circulation model (the Hybrid Coordinate Ocean Model, HYCOM, at 1/25 resolution), which is forced by high frequency winds and includes the astronomical forcing of the sun and the moon, suggests that the flatter slopes of the altimeter may arise from three possible sources, the presence of internal waves, the lack of an inertial subrange in the 70 to 250 km band and noise or submesoscales at small scales. When the wavenumber spectra of SSH and KE are estimated near the internal tide generating regions, the resulting spectra are much flatter than the expectations of QG or SQG theory. If the height and velocity variability are separated into low frequency (periods greater than 2 days) and high frequency (periods less than a day), then a different pattern emerges with a relatively flat wavenumber spectrum at high frequency and a steeper wavenumber spectrum at low frequency. The stationary internal tides can be removed from the altimeter spectrum, which steepens the spectral slopes in the energetic internal wave regions. Away from generating regions where the internal waves

Richman, James↗
NASA NTRS2012

The Distribution of Interplanetary Dust between 0.96 and 1.04 au as Inferred from Impacts on the STEREO Spacecraft Observed by the Heliospheric Imagers

The distribution of dust in the ecliptic plane between 0.96 and 1.04 au has been inferred from impacts on the two Solar Terrestrial Relations Observatory (STEREO) spacecraft through observation of secondary particle trails and unexpected off-points in the heliospheric imager (HI) cameras. This study made use of analysis carried out by members of a distributed webbased citizen science project Solar Stormwatch. A comparison between observations of the brightest particle trails and a survey of fainter trails shows consistent distributions. While there is no obvious correlation between this distribution and the occurrence of individual meteor streams at Earth, there are some broad longitudinal features in these distributions that are also observed in sources of the sporadic meteor population. The different position of the HI instrument on the two STEREO spacecraft leads to each sampling different populations of dust particles. The asymmetry in the number of trails seen by each spacecraft and the fact that there are many more unexpected off-points in the HI-B than in HI-A indicates that the majority of impacts are coming from the apex direction. For impacts causing off-points in the HI-B camera, these dust particles are estimated to have masses in excess of 10 (exp−17) kg with radii exceeding 0.1 μm. For off-points observed in the HI-A images, which can only have been caused by particles travelling from the anti-apex direction, the distribution is consistent with that of secondary 'storm' trails observed by HI-B, providing evidence that these trails also result from impacts with primary particles from an anti-apex source. Investigating the mass distribution for the off-points of both HI-A and HI-B, it is apparent that the differential mass index of particles from the apex direction (causing off-points in HI-B) is consistently above 2. This indicates that the majority of the mass is within the smaller particles of this population. In contrast, the differential mass index of particles from the anti-apex direction (causing off-points in HI-A) is consistently below 2, indicating that the majority of the mass is to be found in larger particles of this distribution.

Davis, C. J.↗
NASA NTRS2012

Test of Shi et al. Method to Infer the Magnetic Reconnection Geometry from Spacecraft Data: MHD Simulation with Guide Field and Antiparallel Kinetic Simulation

When analyzing data from an array of spacecraft (such as Cluster or MMS) crossing a site of magnetic reconnection, it is desirable to be able to accurately determine the orientation of the reconnection site. If the reconnection is quasi-two dimensional, there are three key directions, the direction of maximum inhomogeneity (the direction across the reconnection site), the direction of the reconnecting component of the magnetic field, and the direction of rough invariance (the "out of plane" direction). Using simulated spacecraft observations of magnetic reconnection in the geomagnetic tail, we extend our previous tests of the direction-finding method developed by Shi et al. (2005) and the method to determine the structure velocity relative to the spacecraft Vstr. These methods require data from four proximate spacecraft. We add artificial noise and calibration errors to the simulation fields, and then use the perturbed gradient of the magnetic field B and perturbed time derivative dB/dt, as described by Denton et al. (2010). Three new simulations are examined: a weakly three-dimensional, i.e., quasi-two-dimensional, MHD simulation without a guide field, a quasi-two-dimensional MHD simulation with a guide field, and a two-dimensional full dynamics kinetic simulation with inherent noise so that the apparent minimum gradient was not exactly zero, even without added artificial errors. We also examined variations of the spacecraft trajectory for the kinetic simulation. The accuracy of the directions found varied depending on the simulation and spacecraft trajectory, but all the directions could be found within about 10 for all cases. Various aspects of the method were examined, including how to choose averaging intervals and the best intervals for determining the directions and velocity. For the kinetic simulation, we also investigated in detail how the errors in the inferred gradient directions from the unmodified Shi et al. method (using the unperturbed gradient) depended on the amplitude of the calibration errors. For an accuracy of 3 for the maximum gradient direction, the calibration errors could be as large as 3% of reconnection magnetic field, while for the same accuracy for the minimum gradient direction, the calibration errors could only be as large as 0.03% of the reconnection magnetic field. These results suggest that the maximum gradient direction can normally be determined by the unmodified Shi et al. method, while the modified method or some other method must be used to accurately determine the minimum gradient direction. The structure velocity was found with magnitude accurate to 2% and direction accurate to within 5%.

Denton, R.↗
NASA NTRS2013

Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. We investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Our approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography.

Soil Moisture↗
NASA NTRS2014

The Life and Times of Supervolcanoes: Inferences from Long Valley Caldera

Cataclysmic eruptions of silicic magma from "supervolcanoes" are among the most awe-inspiring natural phenomena found in the geologic record, in terms of size, power, and potential hazard. Based on the repose intervals between eruptions of this magnitude, the magmas responsible for them could accumulate gradually in the shallow crust over time scales that may be in excess of a million years (Smith, 1979; Spera and Crisp, 1981; Shaw, 1985). Pre-eruption magma residence time scales can also be inferred from the age difference between eruption (i.e., using 40Ar/39Ar dating to determine the time when hot erupted material cools to below its Ar closure temperature, 200 to 600 degC) and early pre-eruption crystallization (i.e., zircon saturation temperatures; Reid et al., 1997). I will discuss observations from Long Valley a Quaternary volcanic center in California. Long Valley is a voluminous, dominantly silicic caldera system. Based on extensive dating of accessory minerals (e.g., U-Th-Pb dating of zircon and allanite) along with geochemical and isotopic data we find that silicic magmas begin to crystallize 10's to 100's of thousands of years prior to their eruption and that rhyolites record episodes of punctuated and independent evolution rather than the periodic tapping of a long-lived magma. The more punctuated versus more gradual magma accumulation rates required by the absolute and model ages, respectively, imply important differences in the mass and heat fluxes associated with the generation, differentiation, and storage of voluminous rhyolites and emphasize the need to reconcile the magmatic age differences.

Simon, Justin↗
NASA NTRS2014

Local ISM 3D Distribution and Soft X-ray Background Inferences for Nearby Hot Gas

Three-dimensional (3D) interstellar medium (ISM) maps can be used to locate not only interstellar (IS) clouds, but also IS bubbles between the clouds that are blown by stellar winds and supernovae, and are filled by hot gas. To demonstrate this, and to derive a clearer picture of the local ISM, we compare our recent 3D IS dust distribution maps to the ROSAT diffuse Xray background maps after removal of heliospheric emission. In the Galactic plane, there is a good correspondence between the locations and extents of the mapped nearby cavities and the soft (0.25 keV) background emission distribution, showing that most of these nearby cavities contribute to this soft X-ray emission. Assuming a constant dust to gas ratio and homogeneous 106 K hot gas filling the cavities, we modeled in a simple way the 0.25 keV surface brightness along the Galactic plane as seen from the Sun, taking into account the absorption by the mapped clouds. The data-model comparison favors the existence of hot gas in the solar neighborhood, the so-called Local Bubble (LB). The inferred mean pressure in the local cavities is found to be approx.9,400/cu cm K, in agreement with previous studies, providing a validation test for the method. On the other hand, the model overestimates the emission from the huge cavities located in the third quadrant. Using CaII absorption data, we show that the dust to CaII ratio is very small in those regions, implying the presence of a large quantity of lower temperature (non-X-ray emitting) ionized gas and as a consequence a reduction of the volume filled by hot gas, explaining at least part of the discrepancy. In the meridian plane, the two main brightness enhancements coincide well with the LB's most elongated parts and chimneys connecting the LB to the halo, but no particular nearby cavity is found towards the enhancement in the direction of the bright North Polar Spur (NPS) at high latitude. We searched in the 3D maps for the source regions of the higher energy (0.75 keV) enhancements in the fourth and first quadrants. Tunnels and cavities are found to coincide with the main bright areas, however no tunnel nor cavity is found to match the low-latitude b > or approx. 8deg, brightest part of the NPS. In addition, the comparison between the 3D maps and published spectral data favors a NPS central source region location beyond 230 pc, i.e. at larger distance than usually considered. Those examples illustrate the potential use of more detailed 3D distributions of the nearby ISM for the interpretation of the diffuse soft X-ray background.

Xray Emission↗
NASA NTRS2012

Parametric 3D Atmospheric Reconstruction in Highly Variable Terrain with Recycled Monte Carlo Paths and an Adapted Bayesian Inference Engine

We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.

Monte Carlo↗
NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗
NASA NTRS2015

Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (𝑁), plunge force (𝐹𝑧), and welding speed (𝑉) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (𝑁,𝑉,𝐹𝑧,𝐸𝐹𝐼) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

Mohammad W Dewan↗
NASA NTRS2015

Exercise Sensing and Pose Recovery Inference Tool (ESPRIT) - A Compact Stereo-based Motion Capture Solution For Exercise Monitoring

Crew exercise is important during long-duration space flight not only for maintaining health and fitness but also for preventing adverse health problems, such as losses in muscle strength and bone density. Monitoring crew exercise via motion capture and kinematic analysis aids understanding of the effects of microgravity on exercise and helps ensure that exercise prescriptions are effective. Intelligent Automation, Inc., has developed ESPRIT to monitor exercise activities, detect body markers, extract image features, and recover three-dimensional (3D) kinematic body poses. The system relies on prior knowledge and modeling of the human body and on advanced statistical inference techniques to achieve robust and accurate motion capture. In Phase I, the company demonstrated motion capture of several exercises, including walking, curling, and dead lifting. Phase II efforts focused on enhancing algorithms and delivering an ESPRIT prototype for testing and demonstration.

Lee, Mun Wai↗